{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Meta-Learning with the Rank-Weighted GP Ensemble (RGPE)\n",
    "\n",
    "BoTorch is designed in to be model-agnostic and only requries that a model conform to a minimal interface. This tutorial walks through an example of implementing the rank-weighted Gaussian process ensemble (RGPE) [Feurer, Letham, Bakshy ICML 2018 AutoML Workshop] and using the RGPE in BoTorch to do meta-learning across related optimization tasks.\n",
    "\n",
    "* Original paper: https://arxiv.org/pdf/1802.02219.pdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import math\n",
    "\n",
    "from torch import Tensor\n",
    "\n",
    "torch.manual_seed(29)\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "dtype = torch.float"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Toy Problem\n",
    "* We consider optimizing the following 1-D synthetic function\n",
    "$$f(x, s_i) = \\frac{1}{10}\\bigg(x-1\\bigg)\\bigg(\\sin(x+s_i)+\\frac{1}{10}\\bigg)$$\n",
    "where\n",
    "$$s_i = \\frac{(i+9)\\pi}{8}$$\n",
    "is a task-dependent shift parameter and $i$ is the task index $i \\in [1, t]$.\n",
    "\n",
    "* In this tutorial, we will consider the scenario where we have collected data from 5 prior tasks (referred to as base tasks), which with a different task dependent shift parameter $s_i$.\n",
    "\n",
    "* The goal now is use meta-learning to improve sample efficiency when optimizing a 6th task."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Toy Problem Setup\n",
    "\n",
    "First let's define a function for compute the shift parameter $s_i$ and set the shift amount for the target task."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "NUM_BASE_TASKS = 5\n",
    "\n",
    "def task_shift(task):\n",
    "    \"\"\"\n",
    "    Fetch shift amount for task.\n",
    "    \"\"\"\n",
    "    return math.pi * (task + 9) / 8.0\n",
    "\n",
    "# set shift for target task\n",
    "TARGET_SHIFT = math.pi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, let's define our function $f(x, s_i)$ and set bounds on $x$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "BOUNDS = torch.tensor([[-10.0], [10.0]], dtype=dtype, device=device)\n",
    "\n",
    "def f(X: Tensor, shift: float = TARGET_SHIFT) -> Tensor:\n",
    "    \"\"\"\n",
    "    Torch-compatible objective function for the target_task\n",
    "    \"\"\"\n",
    "    f_X = 0.1*(X-1) * (torch.sin(X + shift) + 0.1)\n",
    "    return f_X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Sample training data for prior base tasks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We sample data from a Sobol sequence to help ensure numerical stability when using a small amount of 1-D data. Sobol sequences help prevent us from sampling a bunch of training points that are close together."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from botorch.utils.sampling import draw_sobol_samples\n",
    "from botorch.utils.transforms import normalize, unnormalize\n",
    "\n",
    "# Sample data for each base task\n",
    "data_by_task = {}\n",
    "for task in range(NUM_BASE_TASKS):\n",
    "    num_training_points = torch.randint(low=15, high=26, size=(1,)).item()\n",
    "    # draw points from a sobol sequence\n",
    "    raw_x = draw_sobol_samples(bounds=BOUNDS, n=num_training_points, q=1, seed=task+5397923).squeeze(1)    \n",
    "    # get observed values\n",
    "    f_x = f(raw_x, task_shift(task)).view(-1)\n",
    "    train_y = f_x + 0.05*torch.randn_like(f_x)\n",
    "    # store training data\n",
    "    data_by_task[task] = {\n",
    "        # scale x to [0, 1]\n",
    "        'train_x': normalize(raw_x, bounds=BOUNDS),\n",
    "        'train_y': train_y,\n",
    "    }         "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let's plot the base tasks and the target task function along with the observed points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(12, 8))\n",
    "x = torch.linspace(-10,10,51)\n",
    "for task in data_by_task:\n",
    "    # plot true function and observed values for base runs\n",
    "    t = ax.plot(\n",
    "        unnormalize(data_by_task[task]['train_x'], bounds=BOUNDS).cpu().numpy(),\n",
    "        data_by_task[task]['train_y'].cpu().numpy(),\n",
    "        '.',\n",
    "        markersize=10,\n",
    "        label=f\"Observed task {task}\",\n",
    "    )\n",
    "    ax.plot(\n",
    "        x.detach().numpy(),\n",
    "        f(x, task_shift(task)).cpu().numpy(),\n",
    "        label=f\"Base task {task}\",\n",
    "        color=t[0].get_color(), \n",
    "    )\n",
    "# plot true target function\n",
    "ax.plot(\n",
    "    x.detach().numpy(),\n",
    "    f(x, TARGET_SHIFT).detach().numpy(),\n",
    "    '--',\n",
    "    label=\"Target task\",\n",
    "\n",
    ")\n",
    "ax.legend(loc=\"lower right\", fontsize=10)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fit base task models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's define a helper function to fit a SingleTaskGP with an inferred noise level given training data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from gpytorch.mlls import ExactMarginalLogLikelihood\n",
    "from botorch.models import SingleTaskGP\n",
    "from botorch.optim.fit import fit_gpytorch_torch\n",
    "from botorch.fit import fit_gpytorch_model\n",
    "\n",
    "\n",
    "def get_fitted_model(train_X: Tensor, train_Y: Tensor) -> SingleTaskGP:\n",
    "    \"\"\"\n",
    "    Fit SingleTaskGP with torch.optim.Adam.\n",
    "    \"\"\"\n",
    "    model = SingleTaskGP(train_X, train_Y)\n",
    "    mll = ExactMarginalLogLikelihood(model.likelihood, model).to(train_X)\n",
    "    fit_gpytorch_model(mll, optimizer=fit_gpytorch_torch, options={\"disp\": False})\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Now let's fit a SingleTaskGP for each base task"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting base model 0\n",
      "Fitting base model 1\n",
      "Fitting base model 2\n",
      "Fitting base model 3\n",
      "Fitting base model 4\n"
     ]
    }
   ],
   "source": [
    "# Fit base model\n",
    "base_model_list = []\n",
    "for task in range(NUM_BASE_TASKS):\n",
    "    print(f\"Fitting base model {task}\")\n",
    "    model = get_fitted_model(data_by_task[task]['train_x'], data_by_task[task]['train_y'])\n",
    "    base_model_list.append(model)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Implement the RGPE\n",
    "\n",
    "The main idea of the RGPE is to estimate the target function as weighted sum of the target model and the base models:\n",
    "$$\\bar f(\\mathbf x | \\mathcal D) =\n",
    "\\sum_{i=1}^{t} w_if^i(\\mathbf x |\\mathcal D_i)$$\n",
    "Importantly, the ensemble model is also a GP:\n",
    "$$\\bar f(\\mathbf x | \\mathcal D) \\sim \\mathcal N\\bigg(\\sum_{i=1}^{t} w_i\\mu_i(\\mathbf x), \\sum_{i=1}^{t}w_i^2\\sigma_i^2\\bigg)$$\n",
    "\n",
    "The weights $w_i$ for model $i$ are based on the the ranking loss between a draw from the model's posterior and the targets. Specifically, the ranking loss for model $i$ is:\n",
    "$$\\mathcal L(f^i, \\mathcal D_t) = \\sum_{j=1}^{n_t}\\sum_{k=1}^{n_t}\\mathbb 1\\bigg[\\bigg(f^i\\big(\\mathbf x^t_j\\big) < f^i\\big(\\mathbf x_k^t\\big)\\bigg)\\oplus \\big(y_j^t < y_k^t\\big)\\bigg]$$\n",
    "where $\\oplus$ is exclusive-or.\n",
    "\n",
    "The loss for the target model is computing using leave-one-out cross-validation (LOOCV) and is given by:\n",
    "$$\\mathcal L(f^t, \\mathcal D_t) = \\sum_{j=1}^{n_t}\\sum_{k=1}^{n_t}\\mathbb 1\\bigg[\\bigg(f^t_{-j}\\big(\\mathbf x^t_j\\big) < f^t_{-j}\\big(\\mathbf x_k^t\\big)\\bigg)\\oplus \\big(y_j^t < y_k^t\\big)\\bigg]$$\n",
    "where $f^t_{-j}$ model fitted to all data from the target task except training example $j$.\n",
    "\n",
    "The weights are then computed as:\n",
    "$$w_i = \\frac{1}{S}\\sum_{s=1}^S\\mathbb 1\\big(i = \\text{argmin}_{i'}l_{i', s}\\big)$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def roll_col(X: Tensor, shift: int) -> Tensor:  \n",
    "    \"\"\"\n",
    "    Rotate columns to right by shift.\n",
    "    \"\"\"\n",
    "    return torch.cat((X[:, -shift:], X[:, :-shift]), dim=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_ranking_loss(f_samps: Tensor, target_y: Tensor) -> Tensor:\n",
    "    \"\"\"\n",
    "    Compute ranking loss for each sample from the posterior over target points.\n",
    "    \n",
    "    Args:\n",
    "        f_samps: `n_samples x n`-dim tensor of samples\n",
    "        target_y: `n`-dim tensor of targets\n",
    "    Returns:\n",
    "        Tensor: `n_samples`-dim tensor containing the ranking loss across each sample\n",
    "    \"\"\"\n",
    "    y_stack = target_y.expand(f_samps.shape[0], *target_y.shape)\n",
    "    rank_loss = torch.zeros(f_samps.shape[0], dtype=torch.long, device=target_y.device)\n",
    "    for i in range(1,target_y.shape[0]):\n",
    "        rank_loss += torch.sum(\n",
    "            (roll_col(f_samps, i) < f_samps) ^ (roll_col(y_stack, i) < y_stack), \n",
    "            dim=1\n",
    "        )\n",
    "    return rank_loss\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Defin a function to do use LOOCV to fit `n` independent GPs (using batch mode) and sample from their posterior at their respective test point. Note one deviation from the original paper is that the kernel hyperparameters are refit for each fold of the LOOCV, whereas the paper uses kernel hyperparameters from the original target model fit on all data points. \n",
    "\n",
    "Check out the [gpytorch batch mode fitting tutorial](https://github.com/cornellius-gp/gpytorch/blob/master/examples/01_Simple_GP_Regression/Simple_Batch_Mode_GP_Regression.ipynb) for more info on batch mode GPs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_target_model_loocv_sample_preds(train_x: Tensor, train_y: Tensor, num_samples: int) -> Tensor:\n",
    "    \"\"\"\n",
    "    Use LOOCV to fit `b=n` independent GPs using batch mode and sample from\n",
    "        their independent posteriors.\n",
    "    \n",
    "    Args:\n",
    "        train_x: `n x d` tensor of training points\n",
    "        train_y: `n` tensor of training targets\n",
    "        num_sample: number of mc samples to draw\n",
    "    \n",
    "    Return: `num_samples x n`-dim tensor of samples for each target point from the corresponding GP\n",
    "        (which was training without that point).\n",
    "    \"\"\"\n",
    "    train_x = train_x.view(-1, 1)\n",
    "    train_y = train_y.view(-1)\n",
    "    batch_size = len(train_x)\n",
    "    masks = torch.eye(len(train_x), dtype=torch.uint8)\n",
    "    train_x_cv = torch.stack([train_x[~m] for m in masks])\n",
    "    train_y_cv = torch.stack([train_y[~m] for m in masks])\n",
    "    test_x_cv = torch.stack([train_x[m] for m in masks])\n",
    "    test_y_cv = torch.stack([train_y[m] for m in masks])\n",
    "    model = get_fitted_model(train_x_cv, train_y_cv)\n",
    "    with torch.no_grad():\n",
    "        # test_x_cv here is `n (batch dimension) x 1 (num points) x 1 (num dimensions)`.\n",
    "        posterior = model.posterior(test_x_cv)\n",
    "        # Since we have a batch mode gp and model.posterior always returns an output dimension,\n",
    "        # the output from `posterior.sample()` here `num_samples x n x 1 x 1`, so let's squeeze\n",
    "        # the last two dimensions.\n",
    "        return posterior.sample(sample_shape=torch.Size([num_samples])).squeeze(-1).squeeze(-1)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from typing import List\n",
    "\n",
    "def compute_rank_weights(\n",
    "    train_x: Tensor, \n",
    "    train_y: Tensor, \n",
    "    base_models: List[SingleTaskGP], \n",
    "    num_samples: int\n",
    ") -> Tensor:\n",
    "    \"\"\"\n",
    "    Compute ranking weights for each base model and the target model (using \n",
    "        LOOCV for the target model). Note: This implementation does not currently \n",
    "        address weight dilution, since we only have a small number of base models.\n",
    "    \n",
    "    Args:\n",
    "        train_x: `n x d` tensor of training points (for target task)\n",
    "        train_y: `n` tensor of training targets (for target task)\n",
    "        base_models: list of `n_t` base models\n",
    "        num_samples: number of mc samples\n",
    "    \n",
    "    Returns:\n",
    "        Tensor: `n_t`-dim tensor with the ranking weight for each model\n",
    "    \"\"\"\n",
    "    ranking_losses = []\n",
    "    # compute ranking loss for each base model\n",
    "    for task in range(len(base_models)):\n",
    "        model = base_models[task]\n",
    "        # compute posterior over training points for target task\n",
    "        posterior = model.posterior(train_x)\n",
    "        f_samps = posterior.sample(sample_shape=torch.Size((num_samples,))).squeeze(-1).squeeze(-1)\n",
    "        # compute and save ranking loss\n",
    "        ranking_losses.append(compute_ranking_loss(f_samps, train_y))\n",
    "    # compute ranking loss for target model using LOOCV\n",
    "    f_samps = get_target_model_loocv_sample_preds(train_x, train_y, num_samples)\n",
    "    ranking_losses.append(compute_ranking_loss(f_samps, train_y))\n",
    "    ranking_loss_tensor = torch.stack(ranking_losses)\n",
    "    # compute best model (minimum ranking loss) for each sample\n",
    "    best_models = torch.argmin(ranking_loss_tensor, dim=0)\n",
    "    # compute proportion of samples for which each model is best\n",
    "    rank_weights = best_models.bincount(minlength=len(ranking_losses)).type_as(train_x)/num_samples\n",
    "    return rank_weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from botorch.models.gpytorch import GPyTorchModel\n",
    "from gpytorch.models import GP\n",
    "from gpytorch.distributions import MultivariateNormal\n",
    "from gpytorch.likelihoods import LikelihoodList\n",
    "from torch.nn import ModuleList\n",
    "\n",
    "\n",
    "class RGPE(GP, GPyTorchModel):\n",
    "    \"\"\"\n",
    "    Rank-weighted GP ensemble. Note: this class inherits from GPyTorchModel which provides an \n",
    "        interface for GPyTorch models in botorch.\n",
    "    \"\"\"\n",
    "    def __init__(self, models: List[SingleTaskGP], weights: Tensor) -> None:\n",
    "        super().__init__()\n",
    "        self.models = ModuleList(models)\n",
    "        for m in models:\n",
    "            if not hasattr(m, \"likelihood\"):\n",
    "                raise ValueError(\n",
    "                    \"RGPE currently only supports models that have a likelihood (e.g. ExactGPs)\"\n",
    "                )\n",
    "        self.likelihood = LikelihoodList(*[m.likelihood for m in models])\n",
    "        self.weights = weights\n",
    "        self.to(dtype=weights.dtype, device=weights.device)\n",
    "        \n",
    "    def forward(self, x: Tensor) -> MultivariateNormal:\n",
    "        # compute posterior for each model\n",
    "        posteriors = [model.posterior(x) for model in self.models]\n",
    "        weighted_means = []\n",
    "        weighted_covars = []\n",
    "        \n",
    "        # filter model with zero weights\n",
    "        # weights on covariance matrices are weight**2\n",
    "        non_zero_weight_indices = (self.weights**2 > 0).nonzero()\n",
    "        non_zero_weights = self.weights[non_zero_weight_indices]\n",
    "        # re-normalize\n",
    "        non_zero_weights /= non_zero_weights.sum()\n",
    "        \n",
    "        for non_zero_weight_idx in range(non_zero_weight_indices.shape[0]):\n",
    "            raw_idx = non_zero_weight_indices[non_zero_weight_idx].item()\n",
    "            posterior = posteriors[raw_idx]\n",
    "            weight = non_zero_weights[non_zero_weight_idx]\n",
    "            weighted_means.append(weight * posterior.mean.squeeze(-1))\n",
    "            # Use lazy covariance matrix\n",
    "            weighted_covars.append(posterior.mvn.lazy_covariance_matrix * weight**2)\n",
    "        # set mean and covariance to be the rank-weighted sum the means and covariances of the\n",
    "        # base models and target model\n",
    "        mean_x = torch.sum(torch.stack(weighted_means), dim=0)\n",
    "        covar_x = gpytorch.lazy.PsdSumLazyTensor(*weighted_covars)\n",
    "        return gpytorch.distributions.MultivariateNormal(mean_x, covar_x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Optimize target function using RGPE + MC-based qEI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Trial 1 of 20\n",
      "Trial 2 of 20\n",
      "Trial 3 of 20\n",
      "Trial 4 of 20\n",
      "Trial 5 of 20\n",
      "Trial 6 of 20\n",
      "Trial 7 of 20\n",
      "Trial 8 of 20\n",
      "Trial 9 of 20\n",
      "Trial 10 of 20\n",
      "Trial 11 of 20\n",
      "Trial 12 of 20\n",
      "Trial 13 of 20\n",
      "Trial 14 of 20\n",
      "Trial 15 of 20\n",
      "Trial 16 of 20\n",
      "Trial 17 of 20\n",
      "Trial 18 of 20\n",
      "Trial 19 of 20\n",
      "Trial 20 of 20\n"
     ]
    }
   ],
   "source": [
    "from botorch.acquisition.monte_carlo import qExpectedImprovement\n",
    "from botorch.sampling.samplers import SobolQMCNormalSampler\n",
    "from botorch.optim.optimize import joint_optimize\n",
    "\n",
    "# suppress GPyTorch warnings about adding jitter\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", \"^.*jitter.*\", category=RuntimeWarning)\n",
    "\n",
    "    \n",
    "best_rgpe_all = []\n",
    "best_random_all = []\n",
    "best_vanilla_ei_all = []\n",
    "N_BATCH = 5\n",
    "INNER_OPTIMIZER_LOOPS = 15\n",
    "NUM_POSTERIOR_SAMPLES = 10\n",
    "RANDOM_INITIALIZATION_SIZE = 4\n",
    "N_TRIALS = 20\n",
    "MC_SAMPLES = 1000\n",
    "N_RESTART_CANDIDATES = 100\n",
    "N_RESTARTS = 5\n",
    "Q_BATCH_SIZE = 1\n",
    "\n",
    "# Average over multiple trials\n",
    "for trial in range(N_TRIALS):\n",
    "    print(f\"Trial {trial + 1} of {N_TRIALS}\")\n",
    "    best_rgpe = []\n",
    "    best_random = [] \n",
    "    best_vanilla_ei = []\n",
    "    # Initial random observations\n",
    "    raw_x = draw_sobol_samples(bounds=BOUNDS, n=RANDOM_INITIALIZATION_SIZE, q=1, seed=trial).squeeze(1)    \n",
    "    train_x = normalize(raw_x, bounds=BOUNDS)\n",
    "    train_y = f(train_x)\n",
    "    vanilla_ei_train_x = train_x.clone()\n",
    "    vanilla_ei_train_y = train_y.clone()\n",
    "    # keep track of the best observed point at each iteration\n",
    "    best_value = train_y.max().item()\n",
    "    best_rgpe.append(best_value)\n",
    "    best_random.append(best_value)\n",
    "    vanilla_ei_best_value = best_value\n",
    "    best_vanilla_ei.append(vanilla_ei_best_value)\n",
    "\n",
    "    # Run N_BATCH rounds of BayesOpt after the initial random batch\n",
    "    for iteration in range(N_BATCH): \n",
    "        target_model = get_fitted_model(train_x, train_y.view(-1))\n",
    "        model_list = base_model_list + [target_model]\n",
    "        rank_weights = compute_rank_weights(train_x, train_y.view(-1), base_model_list, NUM_POSTERIOR_SAMPLES)\n",
    "       \n",
    "        # create model and acquisition function\n",
    "        rgpe_model = RGPE(model_list, rank_weights)\n",
    "        sampler_qei = SobolQMCNormalSampler(num_samples=MC_SAMPLES)\n",
    "        qEI = qExpectedImprovement(model=model, best_f=best_value)\n",
    "        \n",
    "        # optimize\n",
    "        candidate = joint_optimize(\n",
    "            acq_function=qEI,\n",
    "            bounds=torch.tensor([[0.],[1.]], dtype=dtype, device=device),\n",
    "            q=Q_BATCH_SIZE,\n",
    "            num_restarts=N_RESTARTS,\n",
    "            raw_samples=N_RESTART_CANDIDATES,\n",
    "        )\n",
    "\n",
    "        # fetch the new values \n",
    "        new_x = candidate.detach()\n",
    "        new_y = f(unnormalize(new_x, bounds=BOUNDS))\n",
    "\n",
    "        # update training points\n",
    "        train_x = torch.cat((train_x, new_x))\n",
    "        train_y = torch.cat((train_y, new_y))\n",
    "        random_candidate = torch.rand(1, dtype=dtype, device=device)\n",
    "        next_random_best = f(unnormalize(random_candidate, bounds=BOUNDS)).max().item()\n",
    "        best_random.append(max(best_random[-1], next_random_best))\n",
    "\n",
    "        # get the new best observed value\n",
    "        best_value = train_y.max().item()\n",
    "        best_rgpe.append(best_value)\n",
    "\n",
    "        # Run Vanilla EI for comparison\n",
    "        vanilla_ei_model = get_fitted_model(vanilla_ei_train_x, vanilla_ei_train_y.view(-1))\n",
    "        vanilla_ei_sampler = SobolQMCNormalSampler(num_samples=MC_SAMPLES)\n",
    "        vanilla_qEI = qExpectedImprovement(\n",
    "            model=vanilla_ei_model, \n",
    "            best_f=vanilla_ei_best_value, \n",
    "            sampler=vanilla_ei_sampler,\n",
    "        )\n",
    "        vanilla_ei_candidate = joint_optimize(\n",
    "            acq_function=vanilla_qEI,\n",
    "            bounds=torch.tensor([[0.],[1.]], dtype=dtype, device=device),\n",
    "            q=Q_BATCH_SIZE,\n",
    "            num_restarts=N_RESTARTS,\n",
    "            raw_samples=N_RESTART_CANDIDATES,\n",
    "        )\n",
    "        # fetch the new values \n",
    "        vanilla_ei_new_x = vanilla_ei_candidate.detach()\n",
    "        vanilla_ei_new_y = f(unnormalize(vanilla_ei_new_x, bounds=BOUNDS))\n",
    "\n",
    "        # update training points\n",
    "        vanilla_ei_train_x = torch.cat([vanilla_ei_train_x, vanilla_ei_new_x])\n",
    "        vanilla_ei_train_y = torch.cat([vanilla_ei_train_y, vanilla_ei_new_y])\n",
    "\n",
    "        # get the new best observed value\n",
    "        vanilla_ei_best_value = vanilla_ei_train_y.max().item()\n",
    "        best_vanilla_ei.append(vanilla_ei_best_value)\n",
    "        \n",
    "    best_rgpe_all.append(best_rgpe)\n",
    "    best_random_all.append(best_random)\n",
    "    best_vanilla_ei_all.append(best_vanilla_ei)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot best observed value vs iteration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "best_rgpe_all = np.array(best_rgpe_all)\n",
    "best_random_all = np.array(best_random_all)\n",
    "best_vanilla_ei_all = np.array(best_vanilla_ei_all)\n",
    "\n",
    "x = range(RANDOM_INITIALIZATION_SIZE, RANDOM_INITIALIZATION_SIZE + N_BATCH + 1)\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(10, 6))\n",
    "# Plot RGPE - EI\n",
    "ax.errorbar(\n",
    "    x, \n",
    "    best_rgpe_all.mean(axis=0), \n",
    "    yerr=1.96 * best_rgpe_all.std(axis=0) / math.sqrt(N_TRIALS), \n",
    "    label=\"RGPE - EI\", \n",
    "    linewidth=3, \n",
    "    capsize=5,\n",
    "    capthick=3,\n",
    ")\n",
    "# Plot SingleTaskGP - EI\n",
    "ax.errorbar(\n",
    "    x, \n",
    "    best_vanilla_ei_all.mean(axis=0), \n",
    "    yerr=1.96 * best_vanilla_ei_all.std(axis=0) / math.sqrt(N_TRIALS), \n",
    "    label=\"SingleTaskGP - EI\", \n",
    "    linewidth=3,\n",
    "    capsize=5,\n",
    "    capthick=3,\n",
    ")\n",
    "# Plot Random\n",
    "ax.errorbar(\n",
    "    x, \n",
    "    best_random_all.mean(axis=0), \n",
    "    yerr= 1.96 * best_random_all.std(axis=0) / math.sqrt(N_TRIALS), \n",
    "    label=\"Random\", \n",
    "    linewidth=3,\n",
    "    capsize=5,\n",
    "    capthick=3,\n",
    ")\n",
    "ax.set_ylim(bottom=0)\n",
    "ax.set_xlabel('Iteration', fontsize=12)\n",
    "ax.set_ylabel('Best Observed Value', fontsize=12)\n",
    "ax.set_title('Best Observed Value by Iteration', fontsize=12)\n",
    "ax.legend(loc=\"lower right\", fontsize=10)\n",
    "plt.tight_layout()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
